{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Binary classification metrics.\n",
    "You are asked to implement several binary classification metrics and find out if the new model will be useful in production.\n",
    "`model_v1_preds.csv` and `model_v2_preds.csv` contains predictions of current best production model and freshly trained one on test set. Test set was obtained this way:\n",
    "1. Matches we can find with our production models was excluded from candidates set.\n",
    "2. 5 categories was chosen: `Детские товары`, `Дом и сад`, `Строительство и ремонт`, `Товары для взрослых`, `Хобби и творчество`.\n",
    "3. A 10k sample was taken from each category.\n",
    "4. 50k pairs was labeled in crowd system (specially trained people answered the question of whether a pair of products is a match)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import List, Tuple, Dict\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "from sklearn.metrics import auc, precision_recall_curve, roc_auc_score, precision_score, recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-12T08:06:21.724057Z",
     "start_time": "2023-07-12T08:06:21.618375Z"
    }
   },
   "outputs": [],
   "source": [
    "### load models predictions\n",
    "model_v1_preds = pd.read_csv(\"model_v1_preds.csv\")\n",
    "model_v2_preds = pd.read_csv(\"model_v2_preds.csv\")\n",
    "\n",
    "### load real labels\n",
    "real_labels = pd.read_csv(\"real_labels.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-09T10:14:53.897428Z",
     "start_time": "2023-07-09T10:14:53.871813Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Дом и сад                 9001\n",
       "Хобби и творчество        8954\n",
       "Строительство и ремонт    8905\n",
       "Детские товары            8667\n",
       "Товары для взрослых       7895\n",
       "Name: cat2, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "real_labels[\"cat2\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Task 1. ROC AUC.\n",
    "\n",
    "### Point estimate\n",
    "Implement [ROC AUC](https://alexanderdyakonov.wordpress.com/2017/07/28/auc-roc-%D0%BF%D0%BB%D0%BE%D1%89%D0%B0%D0%B4%D1%8C-%D0%BF%D0%BE%D0%B4-%D0%BA%D1%80%D0%B8%D0%B2%D0%BE%D0%B9-%D0%BE%D1%88%D0%B8%D0%B1%D0%BE%D0%BA/) metric, apply it to both models' predictions, compare with metric values of `sklearn.metrics.roc_auc_score`.\n",
    "You can use `sklearn.metrics.auc` to calculate area under the curve. You just need to get coordinates of the points of the curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-12T08:06:30.687159Z",
     "start_time": "2023-07-12T08:06:30.678102Z"
    },
    "code_folding": [],
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def roc_auc_home_made(real_labels: pd.DataFrame, preds: pd.DataFrame) -> float:\n",
    "    df = preds.merge(real_labels, on=[\"product_id_1\", \"product_id_2\"])\n",
    "    ### YOUR CODE HERE\n",
    "    df.sort_values('score', ascending=False, inplace=True)\n",
    "    targets = df.target.values\n",
    "    x, y = [], []\n",
    "    tp, fp, tn, fn = 0, 0, sum(targets == 0), sum(targets == 1)\n",
    "    for target in targets:\n",
    "        if target == 1:\n",
    "            tp += 1\n",
    "            fn -= 1\n",
    "        else:\n",
    "            tn -= 1\n",
    "            fp += 1\n",
    "        fpr = fp / (fp + tn)\n",
    "        tpr = tp / (tp + fn)\n",
    "        x.append(fpr)\n",
    "        y.append(tpr)\n",
    "    return auc(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-12T08:06:57.067794Z",
     "start_time": "2023-07-12T08:06:32.330836Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "assert round(roc_auc_home_made(real_labels, model_v1_preds), 4) \\\n",
    "== round(roc_auc_score(real_labels.target, model_v1_preds.score), 4)\n",
    "assert round(roc_auc_home_made(real_labels, model_v2_preds), 4) \\\n",
    "== round(roc_auc_score(real_labels.target, model_v2_preds.score), 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-26T17:02:45.399427Z",
     "start_time": "2023-07-26T17:02:45.397706Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC v1: 0.8554\n",
      "ROC AUC v2: 0.8714\n"
     ]
    }
   ],
   "source": [
    "print(f\"ROC AUC v1: {round(roc_auc_score(real_labels.target, model_v1_preds.score), 4)}\\n\\\n",
    "ROC AUC v2: {round(roc_auc_score(real_labels.target, model_v2_preds.score), 4)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### Bootstrap\n",
    "#### Drawing conclusions based on a point estimate can be dangerous. You can use bootstrap to get more reliable results.\n",
    "\n",
    "Implement metric with bootstrap using `sklearn.metrics.roc_auc_score`. This metric should take `n_iter` times a subsample of size N with replacement, calculate ROC AUC each time, and return list of metrics and its mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-12T08:21:25.989419Z",
     "start_time": "2023-07-12T08:21:25.985635Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def roc_auc_w_bootstrap(real_labels: pd.DataFrame, \n",
    "                        preds: pd.DataFrame, \n",
    "                        n_iter: int = 1000) -> Tuple[float, List[float]]:\n",
    "    ### YOUR CODE HERE\n",
    "    ### use pandas.sample() with random_state = iteration number to pass assert\n",
    "    df = preds.merge(real_labels, on=[\"product_id_1\", \"product_id_2\"])\n",
    "    N = df.shape[0]\n",
    "    rocauc_vals = []\n",
    "    for i in tqdm(range(n_iter)):\n",
    "        sub_df = df.sample(N, replace=True, random_state=i)\n",
    "        rocauc_vals.append(roc_auc_score(sub_df.target.values, sub_df.score.values))\n",
    "    return np.mean(rocauc_vals), rocauc_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-12T08:22:21.606720Z",
     "start_time": "2023-07-12T08:21:28.950591Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c31e72e3a9e9433ebb975c916090b548",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2d731090ff0c4be6a890cd1b8f60e988",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metric_1, metrics_list_1 = roc_auc_w_bootstrap(real_labels, model_v1_preds, n_iter=1000)\n",
    "metric_2, metrics_list_2 = roc_auc_w_bootstrap(real_labels, model_v2_preds, n_iter=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "assert round(metric_1, 4) == 0.8555\n",
    "assert round(metric_2, 4) == 0.8715"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-26T17:03:34.976129Z",
     "start_time": "2023-07-26T17:03:34.974279Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC v1: 0.8555\n",
      "ROC AUC v2: 0.8715\n"
     ]
    }
   ],
   "source": [
    "print(f\"ROC AUC v1: {round(metric_1, 4)}\\nROC AUC v2: {round(metric_2, 4)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### Plot histograms for each metrics list and write conclusions on these points:\n",
    "1. Which model is better according to results?\n",
    "2. Why is the spread of values is so small that the intervals do not intersect? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-26T17:03:42.981150Z",
     "start_time": "2023-07-26T17:03:42.979449Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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eryZNmqTW1tZwlgMAAGJI2ELO/v37NXPmTK1Zs0ZnnHFGoN0Yo5KSEi1atEhTp05VVlaWysrKdODAAa1bty5c5QAAgBgTtpAzb948TZ48WVdddVVQe11dnRobG5WXlxdoc7vdys3NVVVVVchjtbW1qaWlJWgDAADoSlw4Drp+/Xq9/fbbqq6u7rSvsbFRkuTxeILaPR6P9uzZE/J4RUVFWrx4sfOFAoCtfCnRrgCIOsdXchoaGnTPPffomWeeUf/+/Y/Zz+VyBb02xnRq61BQUCC/3x/YGhoaHK0ZAADYx/GVnJqaGjU1NSk7OzvQdvjwYW3ZskWlpaXauXOnpKMrOqmpqYE+TU1NnVZ3OrjdbrndbqdLBQAAFnN8JefKK6/Ue++9p9ra2sA2duxYzZw5U7W1tRo5cqS8Xq8qKioC72lvb1dlZaVycnKcLgcAAMQox1dykpKSlJWVFdQ2YMAADRo0KNCen5+vwsJCZWZmKjMzU4WFhUpMTNSMGTOcLgcAgJ7p6vomnz9ydeCkheXC4+NZsGCBDh48qLlz56q5uVnjxo1TeXm5kpKSolEOAACwUERCzubNm4Neu1wu+Xw++Xy+SAwPAABiEM+uAgAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACvFRbsA2GXEwo3RLgEAAEms5AAAAEsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlXisA2CJrh6pUb9scgQrAYDegZUcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArBQX7QIAADhl+VK62OePXB0IiZUcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlx0NOUVGRLrnkEiUlJWnIkCGaMmWKdu7cGdTHGCOfz6e0tDQlJCRo4sSJ2r59u9OlAMCpz5dy7A1AlxwPOZWVlZo3b55ef/11VVRU6NChQ8rLy9NXX30V6LNixQoVFxertLRU1dXV8nq9mjRpklpbW50uBwAAxCjHn131yiuvBL1eu3athgwZopqaGn3ve9+TMUYlJSVatGiRpk6dKkkqKyuTx+PRunXrNGfOHKdLAgAAMSjs1+T4/UcfUDZw4EBJUl1dnRobG5WXlxfo43a7lZubq6qqqpDHaGtrU0tLS9AGAADQlbA+hdwYo/nz52vChAnKysqSJDU2NkqSPB5PUF+Px6M9e/aEPE5RUZEWL14czlIBq41YuLHL/fXLJkeoEiCGHOu6KZ5OHjFhXcm588479e677+q5557rtM/lcgW9NsZ0autQUFAgv98f2BoaGsJSLwAAsEfYVnLuuusuvfTSS9qyZYuGDh0aaPd6vZKOruikpqYG2puamjqt7nRwu91yu93hKhUAAFjI8ZBjjNFdd92lDRs2aPPmzcrIyAjan5GRIa/Xq4qKCl100UWSpPb2dlVWVmr58uVOlwMA9uI28lNTV/+/8VWWoxwPOfPmzdO6dev0t7/9TUlJSYFrcFJSUpSQkCCXy6X8/HwVFhYqMzNTmZmZKiwsVGJiombMmOF0OQAAIEY5HnJWrVolSZo4cWJQ+9q1a/WTn/xEkrRgwQIdPHhQc+fOVXNzs8aNG6fy8nIlJSU5XQ4AAIhRYfm66nhcLpd8Pp98Pp/TwwMAAEji2VUAAMBShBwAAGAlQg4AALBSWP/iMexzvL+cCwBAb8FKDgAAsBIhBwAAWImQAwAArMQ1OQB4SjkAK7GSAwAArETIAQAAVuLrKgCINp4mDoQFKzkAAMBKhBwAAGAlQg4AALAS1+TEGB7LAACIFazkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASTyEHcFzHe3p9/bLJEaoEAE4cKzkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFbiFnIAAHoLX0oX+/yRq8MSrOQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJW8gB9BhPKf//urr9F0DEsZIDAACsRMgBAABWIuQAAAArcU0OAJwsrr1BNPDIh5PGSg4AALASIQcAAFiJr6tOMce7VRc4FXELOoBwYCUHAABYiZADAACsRMgBAABW4pocAAiF28RxKuH28pBYyQEAAFYi5AAAACvxdVUUdHW7LLfKwkY9/dMH3GIO9EB3v3q14GsuVnIAAICVCDkAAMBKUQ05K1euVEZGhvr376/s7Gxt3bo1muUAAACLRO2anOeff175+flauXKlxo8frz/84Q+6+uqrtWPHDg0bNixaZUmK7vf/PLYBcBi31gIxK2orOcXFxbrtttt0++23a9SoUSopKVF6erpWrVoVrZIAAIBForKS097erpqaGi1cuDCoPS8vT1VVVZ36t7W1qa2tLfDa7z/6r6+Wlpaw1Hek7UCX+3s67vGOD+DkdPkz2Wa6emP33gfEgjD8ju34WTUmMj9fUQk5X3zxhQ4fPiyPxxPU7vF41NjY2Kl/UVGRFi9e3Kk9PT09bDV2JaUkKsMCOIZu/0wu468aA8cUxp+P1tZWpaSE/+cvqn8nx+VyBb02xnRqk6SCggLNnz8/8PrIkSP6z3/+o0GDBnXq39LSovT0dDU0NCg5OTk8hZ9imJPQmJfOmJPQmJfOmJPQmJfQOuZl7969crlcSktLi8i4UQk5gwcPVt++fTut2jQ1NXVa3ZEkt9stt9sd1Hb66ad3OUZycjIn2LcwJ6ExL50xJ6ExL50xJ6ExL6GlpKREdF6icuFxfHy8srOzVVFREdReUVGhnJycaJQEAAAsE7Wvq+bPn6/Zs2dr7Nixuuyyy7R69Wrt3btXd9xxR7RKAgAAFolayJk2bZq+/PJLLVmyRPv27VNWVpY2bdqk4cOH9+i4brdbDz/8cKevt2IZcxIa89IZcxIa89IZcxIa8xJatObFZSJ1HxcAAEAE8ewqAABgJUIOAACwEiEHAABYiZADAACs1OtCzsqVK5WRkaH+/fsrOztbW7du7bL/s88+qzFjxigxMVGpqam65ZZb9OWXX4bsu379erlcLk2ZMiWo3efzyeVyBW1er9epj9RjTs/JU0891enzulwu/e9//+vRuJEWjXmJtXNFkv773/9q3rx5Sk1NVf/+/TVq1Cht2rSpR+NGWjTmJdbOlYkTJ4b8+Zk8eXKPxo20aMxLbz9XpPD8DJWUlOjcc89VQkKC0tPTde+99zr/e8j0IuvXrzf9+vUza9asMTt27DD33HOPGTBggNmzZ0/I/lu3bjV9+vQxv/3tb83u3bvN1q1bzQUXXGCmTJnSqW99fb0566yzzOWXX25uuOGGoH0PP/ywueCCC8y+ffsCW1NTUzg+4kkLx5ysXbvWJCcnB33effv29WjcSIvWvMTaudLW1mbGjh1rrrnmGrNt2zZTX19vtm7dampra7s9bqRFa15i7Vz58ssvgz7r+++/b/r27WvWrl3b7XEjLVrz0pvPFWPCMy/PPPOMcbvd5tlnnzV1dXXm1VdfNampqSY/P7/b44bSq0LOpZdeau64446gtvPOO88sXLgwZP9HH33UjBw5Mqjt8ccfN0OHDg1qO3TokBk/frz54x//aG6++eaQIWfMmDE9rj8cwjEna9euNSkpKY6OG2nRmpdYO1dWrVplRo4cadrb2x0bN9KiNS+xdq5822OPPWaSkpLM/v37uz1upEVrXnrzuWJMeOZl3rx55vvf/35Qn/nz55sJEyZ0e9xQes3XVe3t7aqpqVFeXl5Qe15enqqqqkK+JycnR5988ok2bdokY4w+//xzvfjii52WR5csWaIzzzxTt9122zHH//DDD5WWlqaMjAxNnz5du3fv7vmH6qFwzsn+/fs1fPhwDR06VNdee63eeeedHo0bSdGalw6xdK689NJLuuyyyzRv3jx5PB5lZWWpsLBQhw8f7va4kRSteekQS+fKtz355JOaPn26BgwY0O1xIyla89KhN54rUvjmZcKECaqpqdGbb74pSdq9e7c2bdoU6OPY+XLCcSjMPv30UyPJ/POf/wxqX7p0qTnnnHOO+b4///nP5rTTTjNxcXFGkrn++uuD/nW1bds2c9ZZZ5l///vfxhgTciVn06ZN5sUXXzTvvvuuqaioMLm5ucbj8ZgvvvjCuQ/YDeGak9dee808/fTTpra21mzZssXceOONJiEhwezatatH40ZKtObFmNg7V84991zjdrvNrbfeat566y3z3HPPmYEDB5rFixf3aNxIida8GBN758o3vfHGG0aSeeONN3o8bqREa16M6b3nijHhnZfHH3/c9OvXL9Dn5z//eY/H/bZes5LTweVyBb02xnRq67Bjxw7dfffdeuihh1RTU6NXXnlFdXV1gedftba2atasWVqzZo0GDx58zDGvvvpq3XjjjRo9erSuuuoqbdy4UZJUVlbm0KfqGSfnRJK++93vatasWRozZowuv/xyvfDCCzrnnHP0xBNPdHvcaIjGvMTauXLkyBENGTJEq1evVnZ2tqZPn65FixZp1apV3R43GqIxL7F2rnzTk08+qaysLF166aU9GjcaojEvvf1ckZyfl82bN2vp0qVauXKl3n77bf3lL3/Ryy+/rF/96lfdHjeUqD276tsGDx6svn37qrGxMai9qalJHo8n5HuKioo0fvx43X///ZKkCy+8UAMGDNDll1+uRx55RJ9//rnq6+t13XXXBd5z5MgRSVJcXJx27typs88+u9NxBwwYoNGjR+vDDz906uN1SzjmJDU1tdN7+vTpo0suuSTwebszbiRFa15Csf1cSU1NVb9+/dS3b9/A+0aNGqXGxka1t7fH7LlyvHmJj4/vdFzbz5UOBw4c0Pr167VkyZIejxtJ0ZqXUHrLuSKFb14efPBBzZ49W7fffrskafTo0frqq6/0s5/9TIsWLXLsfOk1Kznx8fHKzs5WRUVFUHtFRYVycnJCvufAgQPq0yf4I3T8R8cYo/POO0/vvfeeamtrA9v111+vK664QrW1tUpPTw953La2Nn3wwQchf/FFUjjmJBRjjGprawOftzvjRlK05iUU28+V8ePH66OPPgr840CSdu3apdTUVMXHx8fsuXK8eQnF9nOlwwsvvKC2tjbNmjWrx+NGUrTmJZTecq5I4ZuXY/UxR2+Icu58OeEvtiKg43axJ5980uzYscPk5+ebAQMGmPr6emOMMQsXLjSzZ88O9F+7dq2Ji4szK1euNB9//LHZtm2bGTt2rLn00kuPOUaoa3Luu+8+s3nzZrN7927z+uuvm2uvvdYkJSUFxo2mcMyJz+czr7zyivn444/NO++8Y2655RYTFxcX9D3x8caNtmjNS6ydK3v37jWnnXaaufPOO83OnTvNyy+/bIYMGWIeeeSREx432qI1L7F2rnSYMGGCmTZtWrfGjbZozUtvPleMCc+8PPzwwyYpKck899xzZvfu3aa8vNycffbZ5qabbjrhcU9Erwo5xhjzu9/9zgwfPtzEx8ebiy++2FRWVgb23XzzzSY3Nzeo/+OPP27OP/98k5CQYFJTU83MmTPNJ598cszjhwo506ZNM6mpqaZfv34mLS3NTJ061Wzfvt3Jj9UjTs9Jfn6+GTZsmImPjzdnnnmmycvLM1VVVSc1bm8QjXmJtXPFGGOqqqrMuHHjjNvtNiNHjjRLly41hw4dOuFxe4NozEssnis7d+40kkx5eXm3xu0NojEvvf1cMcb5efn666+Nz+czZ599tunfv79JT083c+fONc3NzSc87olwGXOMtXoAAIBTWK+5JgcAAMBJhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWOn/Ab9WPZ9TcoxUAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(metrics_list_1, density=False, bins=30);\n",
    "plt.hist(metrics_list_2, density=False, bins=30);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### ROC AUC macro\n",
    "In our test set there are several different categories. It could be useful to evaluate the quality of models in each category separately, because in production we can have different binarization thresholds for different categories. In the case when different categories have different data distributions, having unique threshold for each category can drastically increase recall (but for ROC AUC it is not necessary).\n",
    "\n",
    "Implement ROC AUC macro metric using `sklearn.metrics.roc_auc_score`. `roc_auc_macro` should calculate ROC AUC for each category separately and then return weighted average. Weighting could be different: \n",
    "1. Weights could be equal to $\\frac{1}{n\\_categories}$ then the metric is the quality of the model on each category on average.\n",
    "2. Weights could be proportional to categories sizes in production — then bigger categories will be more important and metric for this category will have bigger weight.\n",
    "3. If we can somehow determine the weights according to business knowledge, we can use them. Actually not very realistic case.\n",
    "\n",
    "Consider the possibility of each option in your implementation. Also keep in mind that you are provided with categories of two levels: `cat2` and `cat3`. `cat3` are subcategories of `cat2`. If we have enough resources we can set thresholds for large `cat3` and get better recall than with common threshold for entire `cat2`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-12T08:22:43.291890Z",
     "start_time": "2023-07-12T08:22:43.286912Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def roc_auc_macro(\n",
    "    real_labels: pd.DataFrame, preds: pd.DataFrame, \n",
    "    cat_column: str = \"cat2\", cats_weights: Dict[str, float] = None\n",
    ") -> Tuple[float, Dict[str, float]]:\n",
    "    ### YOUR CODE HERE\n",
    "    df = preds.merge(real_labels, on=[\"product_id_1\", \"product_id_2\"])\n",
    "    ans = {}\n",
    "    for cat_name in df[cat_column].unique():\n",
    "        if (cats_weights is not None) and (cats_weights[cat_name] == 0):\n",
    "            continue\n",
    "        cat_df = df.loc[df[cat_column] == cat_name]\n",
    "        ans[cat_name] = roc_auc_score(cat_df.target.values, cat_df.score.values)\n",
    "\n",
    "    if cats_weights is None:\n",
    "        cats_weights = {}\n",
    "        for key in ans:\n",
    "            cats_weights[key] = 1 / len(ans)\n",
    "\n",
    "    metric = sum((cats_weights[key] * ans[key] for key in ans))\n",
    "    return metric, ans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-12T08:22:46.268609Z",
     "start_time": "2023-07-12T08:22:46.117187Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "metric_1, metrics_dict_1 = roc_auc_macro(real_labels, model_v1_preds, cat_column=\"cat2\")\n",
    "metric_2, metrics_dict_2 = roc_auc_macro(real_labels, model_v2_preds, cat_column=\"cat2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "assert round(metric_1, 4) == 0.8501\n",
    "assert round(metric_2, 4) == 0.8666"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-26T17:05:46.550952Z",
     "start_time": "2023-07-26T17:05:46.549038Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC v1: 0.8501\n",
      "ROC AUC v2: 0.8666\n"
     ]
    }
   ],
   "source": [
    "print(f\"ROC AUC v1: {round(metric_1, 4)}\\nROC AUC v2: {round(metric_2, 4)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "**Think about why in this case the values of the macro metric turned out to be worse.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "---\n",
    "It could be useful to look at the metric value in each category. If you have several new models and one is better in one category, another is better in another category, and you have enough resources, you can use different models for different categories in production."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-26T17:06:00.031180Z",
     "start_time": "2023-07-26T17:06:00.029242Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Детские товары': 0.861862400724631,\n",
       " 'Строительство и ремонт': 0.8344794043575187,\n",
       " 'Товары для взрослых': 0.8245881580547112,\n",
       " 'Хобби и творчество': 0.8893195667558019,\n",
       " 'Дом и сад': 0.8402761352579557}"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_dict_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-26T17:06:02.908541Z",
     "start_time": "2023-07-26T17:06:02.906608Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Детские товары': 0.8794014297983738,\n",
       " 'Строительство и ремонт': 0.8356234134075231,\n",
       " 'Товары для взрослых': 0.8626195258358662,\n",
       " 'Хобби и творчество': 0.8985853485163282,\n",
       " 'Дом и сад': 0.8566370439196787}"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_dict_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Let's say we have category weights. Then we can calculate metric using these weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-26T17:06:21.436702Z",
     "start_time": "2023-07-26T17:06:21.434791Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC v1: 0.8501\n",
      "ROC AUC v2: 0.8658\n"
     ]
    }
   ],
   "source": [
    "cats_weights = {\n",
    "    'Детские товары': 0.1,\n",
    "    'Строительство и ремонт': 0.1,\n",
    "    'Товары для взрослых': 0.1,\n",
    "    'Хобби и творчество': 0.2,\n",
    "    'Дом и сад': 0.5\n",
    "}\n",
    "\n",
    "metric_1, metrics_dict_1 = roc_auc_macro(real_labels, model_v1_preds, cat_column=\"cat2\", cats_weights=cats_weights)\n",
    "metric_2, metrics_dict_2 = roc_auc_macro(real_labels, model_v2_preds, cat_column=\"cat2\", cats_weights=cats_weights)\n",
    "\n",
    "print(f\"ROC AUC v1: {round(metric_1, 4)}\\nROC AUC v2: {round(metric_2, 4)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### * Thresholds by `cat3`\n",
    "\n",
    "To find a reliable threshold for one category, on average, you need to get labels for 250 pairs of products. Each label costs 10 rubles, and for reliability three different assessors mark the same pair, so the total cost of one pair is 30 rubles. Based on this, suggest the number of `cat3` in which it is worth choosing a threshold. Suggest what to do with the rest `cat3`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "weights = real_labels.cat3.value_counts().to_dict()\n",
    "sum_weight = 0\n",
    "for key in weights:\n",
    "    if weights[key] <= 250:\n",
    "        weights[key] = 0\n",
    "    sum_weight += weights[key]\n",
    "weights = {key: val/sum_weight for key, val in weights.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC v1: 0.8433\n",
      "ROC AUC v2: 0.8605\n"
     ]
    }
   ],
   "source": [
    "metric_1, _ = roc_auc_macro(real_labels, model_v1_preds, cat_column=\"cat3\",\n",
    "                            cats_weights=weights)\n",
    "metric_2, _ = roc_auc_macro(real_labels, model_v2_preds, cat_column=\"cat3\",\n",
    "                            cats_weights=weights)\n",
    "\n",
    "print(f\"ROC AUC v1: {round(metric_1, 4)}\\nROC AUC v2: {round(metric_2, 4)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Compare metrics with metrics averaged by `cat2` and write conclusions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Тут надо знать среднюю цену товаров, объемы рынков и тд, тут я просто малочисленные категории выкинул."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### It also could be useful to use bootstrap in macro metric to get more reliable results. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 2. PR curve.\n",
    "\n",
    "Implement function that builds `PR curve`. PR curve is built according to the following algorithm:\n",
    "1. The first point of curve is (0, 1).\n",
    "2. Dataframe is sorted in descending order of predictions.\n",
    "3. Starting from the first row, we calculate the precision and recall for the current line and all that are above it. Recall values are x-coordinates of curve, precision values — y-coordinates.\n",
    "4. When recall reaches 1, finish collecting the coordinates.\n",
    "\n",
    "Function should return coordinates of curve."
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![%D0%A1%D0%BD%D0%B8%D0%BC%D0%BE%D0%BA%20%D1%8D%D0%BA%D1%80%D0%B0%D0%BD%D0%B0%202023-07-11%20%D0%B2%2010.30.16.png](attachment:%D0%A1%D0%BD%D0%B8%D0%BC%D0%BE%D0%BA%20%D1%8D%D0%BA%D1%80%D0%B0%D0%BD%D0%B0%202023-07-11%20%D0%B2%2010.30.16.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-12T08:23:41.645760Z",
     "start_time": "2023-07-12T08:23:41.638998Z"
    }
   },
   "outputs": [],
   "source": [
    "def pr_curve(real_labels: pd.DataFrame, preds: pd.DataFrame) -> Tuple[np.array, np.array]:\n",
    "    ### YOUR CODE HERE\n",
    "    df = preds.merge(real_labels, on=[\"product_id_1\", \"product_id_2\"])\n",
    "    df.sort_values('score', ascending=False, inplace=True)\n",
    "    targets = df.target.values\n",
    "    x, y = [0], [1]\n",
    "    tp, fp, tn, fn = 0, 0, sum(targets == 0), sum(targets == 1)\n",
    "    for target in targets:\n",
    "        if target == 1:\n",
    "            tp += 1\n",
    "            fn -= 1\n",
    "        else:\n",
    "            tn -= 1\n",
    "            fp += 1\n",
    "        pr = tp / (tp + fp)\n",
    "        re = tp / (tp + fn)\n",
    "        x.append(re)\n",
    "        y.append(pr)\n",
    "    return np.array(y), np.array(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use `sklearn.metrics.auc` to calculate area under the curve. You just need to get coordinates of the points of the curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-12T08:24:15.555012Z",
     "start_time": "2023-07-12T08:23:50.623732Z"
    }
   },
   "outputs": [],
   "source": [
    "my_prec, my_rec = pr_curve(real_labels, model_v1_preds)\n",
    "lib_prec, lib_rec, _ = precision_recall_curve(real_labels.target, model_v1_preds.score)\n",
    "lib_prec = lib_prec[::-1]\n",
    "lib_rec = lib_rec[::-1]\n",
    "assert (my_prec - lib_prec).sum() == (my_rec - lib_rec).sum() == 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recall (Precision = X%) / Precision (Recall = X%)\n",
    "Implement function that calculates recall of a model for a certain precision and of a model for a certain recall. Compare `v1` and `v2` by these metrics.\n",
    "You can use `sklearn.metrics.precision_recall_curve` to get curve coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-12T09:27:18.348936Z",
     "start_time": "2023-07-12T09:27:18.343799Z"
    }
   },
   "outputs": [],
   "source": [
    "def recall_at_precision(\n",
    "    real_labels: pd.DataFrame, preds: pd.DataFrame, precision_lvl: float = 0.95\n",
    ") -> float:\n",
    "    ### YOUR CODE HERE\n",
    "    prec, rec = pr_curve(real_labels, preds)\n",
    "    ans = rec[0]\n",
    "    for pr, re in zip(prec, rec):\n",
    "        if pr >= precision_lvl:\n",
    "            ans = max(ans, re)\n",
    "    return ans\n",
    "\n",
    "\n",
    "def precision_at_recall(\n",
    "    real_labels: pd.DataFrame, preds: pd.DataFrame, recall_lvl: float = 0.5\n",
    ") -> float:\n",
    "    ### YOUR CODE HERE\n",
    "    prec, rec = pr_curve(real_labels, preds)\n",
    "    ans = prec[-1]\n",
    "    for pr, re in zip(reversed(prec), reversed(rec)):\n",
    "        if re >= recall_lvl:\n",
    "            ans = max(ans, pr)\n",
    "    return ans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Recall at precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert round(recall_at_precision(real_labels, model_v1_preds, precision_lvl=0.95), 4) == 0.0123\n",
    "assert round(recall_at_precision(real_labels, model_v2_preds, precision_lvl=0.95), 4) == 0.0551"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-26T17:21:03.639578Z",
     "start_time": "2023-07-26T17:21:03.637756Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "v1 recall at 95% precision: 0.0123\n",
      "v2 recall at 95% precision: 0.0551\n"
     ]
    }
   ],
   "source": [
    "print(f\"v1 recall at 95% precision: {round(recall_at_precision(real_labels, model_v1_preds, precision_lvl=0.95), 4)}\")\n",
    "print(f\"v2 recall at 95% precision: {round(recall_at_precision(real_labels, model_v2_preds, precision_lvl=0.95), 4)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-26T17:21:07.213264Z",
     "start_time": "2023-07-26T17:21:07.211536Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "v1 recall at 75% precision: 0.2608\n",
      "v2 recall at 75% precision: 0.4179\n"
     ]
    }
   ],
   "source": [
    "print(f\"v1 recall at 75% precision: {round(recall_at_precision(real_labels, model_v1_preds, precision_lvl=0.75), 4)}\")\n",
    "print(f\"v2 recall at 75% precision: {round(recall_at_precision(real_labels, model_v2_preds, precision_lvl=0.75), 4)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Precision at recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert round(precision_at_recall(real_labels, model_v1_preds, recall_lvl=0.5), 4) == 0.6265\n",
    "assert round(precision_at_recall(real_labels, model_v2_preds, recall_lvl=0.5), 4) == 0.7031"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-26T17:21:11.238226Z",
     "start_time": "2023-07-26T17:21:11.236493Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "v1 precision at 50% recall: 0.6265\n",
      "v2 precision at 50% recall: 0.7031\n"
     ]
    }
   ],
   "source": [
    "print(f\"v1 precision at 50% recall: {round(precision_at_recall(real_labels, model_v1_preds, recall_lvl=0.5), 4)}\")\n",
    "print(f\"v2 precision at 50% recall: {round(precision_at_recall(real_labels, model_v2_preds, recall_lvl=0.5), 4)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-26T17:21:16.788395Z",
     "start_time": "2023-07-26T17:21:16.786592Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "v1 precision at 30% recall: 0.729\n",
      "v2 precision at 30% recall: 0.8193\n"
     ]
    }
   ],
   "source": [
    "print(f\"v1 precision at 30% recall: {round(precision_at_recall(real_labels, model_v1_preds, recall_lvl=0.3), 4)}\")\n",
    "print(f\"v2 precision at 30% recall: {round(precision_at_recall(real_labels, model_v2_preds, recall_lvl=0.3), 4)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "v1_precision, v1_recall = pr_curve(real_labels, model_v1_preds)\n",
    "v2_precision, v2_recall = pr_curve(real_labels, model_v2_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-26T17:21:21.949445Z",
     "start_time": "2023-07-26T17:21:21.947549Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 7))\n",
    "plt.plot(v2_recall, v2_precision, label=\"v2\")\n",
    "plt.plot(v1_recall, v1_precision, label=\"v1\")\n",
    "plt.axhline(y=0.95, color='grey', linestyle='-.', label=\"95% prec\")\n",
    "plt.axhline(y=0.75, color='grey', linestyle='--', label=\"75% prec\")\n",
    "plt.axvline(x=0.5, color='tan', linestyle='-.', label=\"50% recall\")\n",
    "plt.axvline(x=0.3, color='tan', linestyle='--', label=\"30% recall\")\n",
    "plt.legend(fontsize=14)\n",
    "plt.xlabel(\"Recall\", fontsize=15)\n",
    "plt.ylabel(\"Precision\", fontsize=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PR AUC\n",
    "Implement PR AUC metric, using `sklearn.metrics.precision_recall_curve` and `sklearn.metrics.auc`. Compare models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-12T10:17:43.529657Z",
     "start_time": "2023-07-12T10:17:43.526437Z"
    }
   },
   "outputs": [],
   "source": [
    "def pr_auc(real_labels: pd.DataFrame, preds: pd.DataFrame) -> float:\n",
    "    ### YOUR CODE HERE\n",
    "    df = preds.merge(real_labels, on=[\"product_id_1\", \"product_id_2\"])\n",
    "    targets = df.target.values\n",
    "    scores = df.score.values\n",
    "    precision, recall, thresholds = precision_recall_curve(targets, scores)\n",
    "    return auc(recall, precision)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-26T18:12:18.987981Z",
     "start_time": "2023-07-26T18:12:18.986157Z"
    }
   },
   "outputs": [],
   "source": [
    "assert round(pr_auc(real_labels, model_v1_preds), 4) == 0.6005\n",
    "assert round(pr_auc(real_labels, model_v2_preds), 4) == 0.6618"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PR AUC (Precision = X%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-12T10:24:41.508125Z",
     "start_time": "2023-07-12T10:24:41.503322Z"
    }
   },
   "outputs": [],
   "source": [
    "def pr_auc_at_precision(\n",
    "    real_labels: pd.DataFrame, preds: pd.DataFrame, precision_lvl: float = 0.95\n",
    ") -> float:\n",
    "    ### YOUR CODE HERE\n",
    "    prec, rec = pr_curve(real_labels, preds)\n",
    "    ans = auc(rec[:2], prec[:2])\n",
    "    for i in range(2, len(prec)):\n",
    "        if prec[i - 1] >= precision_lvl:\n",
    "            ans = auc(rec[:i], prec[:i])\n",
    "    return ans\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-26T18:14:50.486879Z",
     "start_time": "2023-07-26T18:14:50.484893Z"
    }
   },
   "outputs": [],
   "source": [
    "assert round(pr_auc_at_precision(real_labels, model_v1_preds), 4) == 0.0118\n",
    "assert round(pr_auc_at_precision(real_labels, model_v2_preds), 4) == 0.0537"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PR AUC (Precision = X%) macro\n",
    "\n",
    "Let's say we have two use cases:\n",
    "1. We want to give equal weights to each of groups.\n",
    "2. We have production-like distribution in our target_df and we want to give each group the weight proportional to it size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-12T12:16:48.110767Z",
     "start_time": "2023-07-12T12:16:48.099404Z"
    }
   },
   "outputs": [],
   "source": [
    "def pr_auc_at_precision_macro(\n",
    "    target_df: pd.DataFrame,\n",
    "    predictions_df: pd.DataFrame,\n",
    "    prec_level: float = 0.95,\n",
    "    cat_column: str = \"cat2\",\n",
    "    weighted: bool = True\n",
    ") -> Tuple[float, Dict[str, float]]:\n",
    "\n",
    "    ### YOUR CODE HERE\n",
    "    ans_dict = {}\n",
    "    count_dict = {}\n",
    "    for col_val in target_df[cat_column].unique():\n",
    "        inds = target_df[cat_column] == col_val\n",
    "        count_dict[col_val] = inds.sum() / len(inds)\n",
    "        ans_dict[col_val] = pr_auc_at_precision(target_df.loc[inds],\n",
    "                                                predictions_df.loc[inds],\n",
    "                                                prec_level)\n",
    "    if weighted:\n",
    "        ans = sum(count_dict[col] * pr for col, pr in ans_dict.values())\n",
    "    else:\n",
    "        ans = np.mean(list(ans_dict.values()))\n",
    "    return ans, ans_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-26T18:22:21.111221Z",
     "start_time": "2023-07-26T18:22:21.109228Z"
    }
   },
   "outputs": [],
   "source": [
    "metric_1, metrics_by_cat_1 = pr_auc_at_precision_macro(\n",
    "    real_labels, model_v1_preds, weighted=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-07-26T18:19:16.161199Z",
     "start_time": "2023-07-26T18:19:16.159392Z"
    }
   },
   "outputs": [],
   "source": [
    "metric_2, metrics_by_cat_2 = pr_auc_at_precision_macro(\n",
    "    real_labels, model_v2_preds, weighted=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert round(metric_1, 4) == 0.0194\n",
    "assert round(metric_2, 4) == 0.081"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Детские товары': 0.01964183363760228,\n",
       " 'Строительство и ремонт': 0.008171603677221655,\n",
       " 'Товары для взрослых': 0.0006079027355623101,\n",
       " 'Хобби и творчество': 0.06569724099588267,\n",
       " 'Дом и сад': 0.002861230329041488}"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_by_cat_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Детские товары': 0.05895282030627212,\n",
       " 'Строительство и ремонт': 0.07272111290161434,\n",
       " 'Товары для взрослых': 0.05753678879586697,\n",
       " 'Хобби и творчество': 0.16970857437434023,\n",
       " 'Дом и сад': 0.04624892243476453}"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics_by_cat_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 3. Conclusions.\n",
    "\n",
    "Having different metrics, analyze the relevance of each in relation to the task, selecting the best model for production. Think about the different business needs that determine which metric to consider as the main one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Мы видим, что абсолютно по всем наблюдаемым метрикам, вторая модель превосходит первую. Даже по Roc-auc'у в отдельных категориях, новая модель превосходит старую во всех категориях. Соответственно, мы однозначно можем выкатывать новую модель.\n",
    "\n",
    "По поводу того, какую метрику считать главной, это вопрос к бизнесу. Для некоторых задач, например поиска дубликатов, нам важнее precision, и можно использовать метрики типо recall @ (precision = 95%), а для поиска продуктов по замещению, использовать precision @ (recall = 75%). Надо смотреть на цены товаров в разных категориях, на объемы продаж в разных категориях, и на убытки/прибыли бизнеса в от разных типов решения задачи мэтчинга. "
   ]
  }
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